Coordinate Descent Converges Faster with the Gauss-Southwell Rule Than Random Selection

no code implementations • 1 Jun 2015 • Julie Nutini, Mark Schmidt, Issam H. Laradji, Michael Friedlander, Hoyt Koepke

There has been significant recent work on the theory and application of randomized coordinate descent algorithms, beginning with the work of Nesterov [SIAM J.

An Algorithmic Theory of Dependent Regularizers, Part 1: Submodular Structure

no code implementations • 6 Dec 2013 • Hoyt Koepke, Marina Meila

In Part 2, we describe the full regularization path of a class of penalized regression problems with dependent variables that includes the graph-guided LASSO and total variation constrained models.

BIG-bench Machine Learning